least-squares parameter estimator
Empirical likelihood based inference in Poisson autoregressive model with conditional moment restrictions
... the parameter estimation problem for Poisson autoregressive model with condi- tional moment ...likelihood estimator, the least squares estimator and the weighted least ...
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Regularized Nonlinear Least Trimmed Squares Estimator in the Presence of Multicollinearity and Outliers
... simultaneous parameter model estimation and ...the least squares loss func- tion which is influenced by outliers ...the least squares loss function with the robust technique ...
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Two stage weighted least squares estimator of the conditional mean of observation driven time series models
... Considerable interest has been paid in recent years to modeling and forecasting daily realized volatility, which is defined as an integrate variability of high frequency intra-day asset returns (see e.g. ...
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Generalized Random Coefficient Estimators of Panel Data Models: Asymptotic and Small Sample Properties
... pooled least squares (classical pooling) estimator is the best linear unbiased estimator (BLUE) under the classical assumptions as in the general linear regression ...regression ...
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Total Least Squares Fitting the Three-Parameter Inverse Weibull Density
... for parameter estimation is the least squares ...ordinary least squares (OLS) fitting problem for the three-parameter inverse Weibull density is considered by Maruši´c et ...
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Restricted estimator in two seemingly unrelated regression model
... The least squares estimator performs poorly in the presence of ...consider parameter estimation in addition to the sample information such as under some exact or stochastic restrictions on the ...
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Consistency of the structured total least squares estimator in a multivariate errors in variables model
... An optimization problem equivalent to the one defining the STLS estimator was derived by analytic minimization over the nuisance parameters, defining the structure. The resulting problem min X∈ X Q(X) has as ...
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Using wavelets to obtain a consistent ordinary least squares estimator of the long memory parameter
... dierencing parameter that exist calculate either the exact or approximate maximum likelihood estimator of the fractional dierencing ...OLS estimator do not require the inversion of the covariance ...
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On the computation of the structured total least squares estimator
... One approach, see References [11–13], to derive special purpose algorithms is to apply an iterative procedure, in which the constraint of (4) is linearized around the current approxima- tion point and an equality ...
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An inverse problem formulation methodology for stochastic models
... For our motivating example model, patients in a hospital unit are classified by compartments or states as either uncolonized 𝑈 (𝑡), VRE colonized 𝐶(𝑡), or VRE colonized in isolation 𝐽(𝑡), as depicted in the compartmental ...
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Development of a robust hybrid estimator using partial least squares regression and artificial neural networks.
... The aim of this paper is ro develop a robust inferentjal estimator by usjng hybrid PLS-ANN model based on on-line measuements of process variables, such as flow raies and temperatur[r] ...
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A plug in averaging estimator for regressions with heteroskedastic errors
... averaging estimator for the linear regression model with het- eroskedastic ...averaging estimator in a local asymptotic framework, and then choose the optimal weights by minimizing the ...plug-in ...
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ESTIMATORS OF LINEAR REGRESSION MODEL WITH AUTOCORRELATED ERRROR TERMS AND PREDICTION USING CORRELATED UNIFORM REGRESSORS
... These figures reveal that the performances of COR and ML estimators at each level of multicollinearity over the levels of autocorrelation are convex – like while that of the OLS and PC estimators are concave. Also, as ...
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ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
... the least squares method, relative least squares, ridge regression, moment and modified moment estimators, maximum and modified maximum likelihood estimators to estimate the two ...
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The large deviation for the least squares estimator of nonlinear regression model based on WOD errors
... regression model, one can refer to Ibregimov and Has’minskii [], Ivanov and Leonenko [], Ivanov [], and so on. In this paper, the large deviation results for the least squares estimator of the ...
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International Journal of Data Envelopment Analysis and *Operations Research*
... distribution parameter λ are studied, Maximum Likelihood estimator, Moments estimator, Percentile estimator, least square estimator and weighted least square ...Percentile ...
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Parameter estimation of tapioca starch hydrolysis process: application of least squares and genetic algorithm
... The influence of the user defined parameters (population size, mutation rate and crossover rate) of the genetic algorithm was studied. The GA performance was evaluated based on the fitness and error index values. In the ...
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The Spurious Effect of ARCH Errors on Linearity Tests:A Theoretical Note and an Alternative Maximum Likelihood Approach
... To illustrate the effect of the bias of the least squares variance estimator on the performance of linearity tests, we estimate the empirical size of the tests (i.e., the number of times each test ...
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Consistent estimation in an implicit quadratic measurement error model
... The statement of Theorem 4 is one of the main contributions of the paper. Adjust- ment procedures, similar to the one described in Section 4, already appeared in the literature; 7rst proposed in Kanatani (1994) and later ...
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Minimization of Error in Exponential Model Estimation via Jackknife Algorithm
... Liu estimator in linear regression ...Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic ...Liu estimator. Under the mean square error ...
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